DocumentCode
2553553
Title
A graph based transductive ranking algorithm
Author
Pan, Zhibin ; Wei, Xiaoyan
Author_Institution
Coll. of Sci., Huazhong Agric. Univ., Wuhan, China
fYear
2012
fDate
29-31 May 2012
Firstpage
991
Lastpage
994
Abstract
Semi-supervised ranking is a newly developed machine learning problem. In this paper, based on the graph constructed on both labeled and unlabeled data points, we propose a novel semi-supervised ranking algorithm in the transductive setting via a semi-supervised regression model. We also derive the solution in an explicit form for this model. Experiments on two QSAR data sets demonstrate its utility and effectiveness.
Keywords
QSAR; graph theory; learning (artificial intelligence); regression analysis; QSAR data sets; graph construction; graph-based transductive ranking algorithm; labeled data points; machine learning problem; semisupervised ranking; semisupervised regression model; transdutive setting; unlabeled data points; Algorithm design and analysis; Biology; Compounds; Correlation; Laplace equations; Machine learning; Standards; Graph Laplacian; Quantitative Structure-Activity Relationship; Ranking; Semi-supervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
Conference_Location
Sichuan
Print_ISBN
978-1-4673-0025-4
Type
conf
DOI
10.1109/FSKD.2012.6234360
Filename
6234360
Link To Document